Anton Dmitriev created IGNITE-10201:
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Summary: ML: TensorFlow model inference on Apache Ignite
Key: IGNITE-10201
URL: https://issues.apache.org/jira/browse/IGNITE-10201
Project: Ignite
Issue Type: New Feature
Components: ml
Affects Versions: 2.8
Reporter: Anton Dmitriev
Assignee: Anton Dmitriev
Fix For: 2.8
Machine learning pipeline consists of two stages: *model training* and *model
inference* _(model training is a process of training a model using existing
data with known target values, model inference is a process of making
predictions on a new data using trained model)._
It's important that a model can be trained in one environment/system and after
that is used for inference in another. A trained model is an immutable object
without any side-effects (a pure mathematical function in math language). As
result of that, an inference process has an excellent linear scalability
characteristics because different inferences can be done in parallel in
different threads or on different nodes.
The goal of "TensorFlow model inference on Apache Ignite" is to allow user to
easily import pre-trained TensorFlow model into Apache Ignite, distribute it
across nodes in a cluster, provide a common interface to call these models to
make inference and finally perform load balancing so that all node resources
are properly utilized.
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